{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validating SVR Model for Electricity Demand Forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This iPython notebook composed by Justin Elszasz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook describes the process of training a support vector regression model for predicting electricity consumption based on several weather variables, the hour of the day, and whether the day was a weekend/holiday/work-from-home-day or just a normal weekday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import history\n",
    "import errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# some nice colors - like Dad always said, it's better to look good than to be good\n",
    "gray_light = '#d4d4d2'\n",
    "gray_med = '#737373'\n",
    "red_orange = '#ff3700'\n",
    "blue_light = '#1ebed7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hum</th>\n",
       "      <th>tempF</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>timestamp_end</th>\n",
       "      <th>wspdMPH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-18 00:00:00</th>\n",
       "      <td> 56</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.13</td>\n",
       "      <td> 2014-01-18 00:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 01:00:00</th>\n",
       "      <td> 61</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 0.98</td>\n",
       "      <td> 2014-01-18 01:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 02:00:00</th>\n",
       "      <td> 61</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 0.94</td>\n",
       "      <td> 2014-01-18 02:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 03:00:00</th>\n",
       "      <td> 65</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.11</td>\n",
       "      <td> 2014-01-18 03:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 04:00:00</th>\n",
       "      <td> 70</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.34</td>\n",
       "      <td> 2014-01-18 04:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     hum  tempF  USAGE        timestamp_end  wspdMPH\n",
       "timestamp                                                           \n",
       "2014-01-18 00:00:00   56   39.2   1.13  2014-01-18 00:59:00    4.588\n",
       "2014-01-18 01:00:00   61   39.2   0.98  2014-01-18 01:59:00    4.588\n",
       "2014-01-18 02:00:00   61   39.2   0.94  2014-01-18 02:59:00    4.588\n",
       "2014-01-18 03:00:00   65   39.2   1.11  2014-01-18 03:59:00    4.588\n",
       "2014-01-18 04:00:00   70   39.2   1.34  2014-01-18 04:59:00    4.588"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the BGE hourly electricity data and weather data using import_funcs.py class\n",
    "elec = pd.read_csv('data/elec_hourly_oldApt_2014-04-30.csv', parse_dates=True, index_col=0)\n",
    "weather = pd.read_csv('data/weather_2015-02-01.csv', parse_dates=True, index_col=0)\n",
    "\n",
    "# Merge into one Pandas dataframe\n",
    "elec_and_weather = pd.merge(weather, elec, left_index=True, right_index=True)\n",
    "\n",
    "# Remove unnecessary fields from dataframe\n",
    "del elec_and_weather['tempm'], elec_and_weather['COST'], elec_and_weather['UNITS']\n",
    "del elec_and_weather['precipm']\n",
    "\n",
    "# Convert windspeed to MPH for my feeble brain to interpret\n",
    "elec_and_weather['wspdMPH'] = elec_and_weather['wspdm'] * 0.62\n",
    "del elec_and_weather['wspdm']\n",
    "\n",
    "elec_and_weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to distinguish typical weekdays from any weekends, holidays and days I worked from home.  I don't think any of those categories have enough of their own examples in order to qualify them as their own set so for now all normal weekdays are 0, all holidays, weekends, and work-from-home days are 1's.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Set weekends and holidays to 1, otherwise 0\n",
    "elec['Atypical_Day'] = np.zeros(len(elec))\n",
    "\n",
    "# Weekends\n",
    "elec['Atypical_Day'][(elec.index.dayofweek==5)|(elec.index.dayofweek==6)] = 1\n",
    "\n",
    "# Holidays, days I worked from home\n",
    "holidays = ['2014-01-01','2014-01-20']\n",
    "work_from_home = ['2014-01-21','2014-02-13','2014-03-03','2014-04-04']\n",
    "\n",
    "for i in range(len(holidays)):\n",
    "    elec['Atypical_Day'][elec.index.date==np.datetime64(holidays[i])] = 1\n",
    "\n",
    "for i in range(len(work_from_home)):\n",
    "    elec['Atypical_Day'][elec.index.date==np.datetime64(work_from_home[i])] = 1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this case each hour of the day is a categorical variable, not continuous. The doing the analysis will require \"yes\" or \"no\" corresponding to each hour of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2014-01-18 00:00:00    0\n",
       "2014-01-18 01:00:00    0\n",
       "2014-01-18 02:00:00    0\n",
       "2014-01-18 03:00:00    1\n",
       "2014-01-18 04:00:00    0\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new column for each hour of day, assign 1 if index.hour is corresponding hour of column, 0 otherwise\n",
    "\n",
    "for i in range(0,24):\n",
    "    elec[i] = np.zeros(len(elec['USAGE']))\n",
    "    elec[i][elec.index.hour==i] = 1\n",
    "    \n",
    "# Example 3am\n",
    "elec[3][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I want to predict the next hour's energy consumption, any given X vector/Y target pair in the training data should provide the current hour's electricity use (target) with the previous hour's (or however many past hours are used) weather data and usage (X vector).  UPDATE: I moved this append procedure to it's own .py class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "history.py:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['%s_t-%i'%(var,j)][i] = df[var][i-j]\n"
     ]
    }
   ],
   "source": [
    "# MOVED TO history.py\n",
    "\n",
    "# Set number of hours prediction is in advance\n",
    "n_hours_advance = 1\n",
    "# Set number of historic hours used\n",
    "n_hours_window = 1\n",
    "\n",
    "elec = history.append_history(elec,'USAGE',n_hours_advance,n_hours_window)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is time series data, it makes more sense to define training and testing periods.  If it weren't time series, I could select a random sample to segregate for a testing set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define training and testing periods\n",
    "\n",
    "gridsearch_start = '18-jan-2014'\n",
    "gridsearch_end = '25-jan-2014'\n",
    "train_start = '26-jan-2014'\n",
    "train_end = '24-march-2014'\n",
    "test_start = '25-march-2014'\n",
    "test_end = '31-march-2014'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn import cross_validation\n",
    "from sklearn import preprocessing as pre\n",
    "from sklearn import grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set up dataframes for building SVR model\n",
    "\n",
    "# Need to keep only the t-1 variables for predicting the next hour\n",
    "X_gridsearch_df = elec[gridsearch_start:gridsearch_end]\n",
    "del X_gridsearch_df['USAGE']\n",
    "\n",
    "X_scaling_df = elec[gridsearch_start:train_end]\n",
    "del X_scaling_df['USAGE']\n",
    "\n",
    "X_train_df = elec[train_start:train_end]\n",
    "del X_train_df['USAGE']\n",
    "\n",
    "X_test_df = elec[test_start:test_end]\n",
    "del X_test_df['USAGE']\n",
    "\n",
    "y_gridsearch_df = elec['USAGE'][gridsearch_start:gridsearch_end]\n",
    "y_train_df = elec['USAGE'][train_start:train_end]\n",
    "y_test_df = elec['USAGE'][test_start:test_end]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Numpy arrays for sklearn\n",
    "\n",
    "X_gridsearch = np.array(X_gridsearch_df)\n",
    "X_scaling = np.array(X_scaling_df)\n",
    "X_train = np.array(X_train_df)\n",
    "X_test = np.array(X_test_df)\n",
    "\n",
    "y_train = np.array(y_train_df)\n",
    "y_test = np.array(y_test_df)\n",
    "y_gridsearch = np.array(y_gridsearch_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing.  StandardScaler() from the \"preprocessing\" module in \"sklearn\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/utils/validation.py:332: UserWarning: StandardScaler assumes floating point values as input, got object\n",
      "  \"got %s\" % (estimator, X.dtype))\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for float(): 2014-03-24 23:59:00",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-0cb3b02ab6e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpreprocessing\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_scaling\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mX_train_scaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX_test_scaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/preprocessing/data.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwarn_if_not_float\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_mean\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for float(): 2014-03-24 23:59:00"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing as pre\n",
    "\n",
    "scaler = pre.StandardScaler().fit(X_scaling)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's where things digress from the original \"hourly_forecast_SVR.ipynb\" notebook. The optimum C and gamma values are found through this grid search/cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 2 µs, total: 5 µs\n",
      "Wall time: 7.87 µs\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for float(): 2014-01-25 23:59:00",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-922b15e9d9d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mSVR_model_OptParams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSVR_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mSVR_model_OptParams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_gridsearch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_gridsearch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/grid_search.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m         \"\"\"\n\u001b[0;32m--> 596\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/grid_search.pyc\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, parameter_iterable)\u001b[0m\n\u001b[1;32m    376\u001b[0m                                     \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m                                     self.fit_params, return_parameters=True)\n\u001b[0;32m--> 378\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m             for train, test in cv)\n\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    651\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 653\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpre_dispatch\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"all\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch\u001b[0;34m(self, func, args, kwargs)\u001b[0m\n\u001b[1;32m    398\u001b[0m         \"\"\"\n\u001b[1;32m    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_verbosity_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, func, args, kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/cross_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters)\u001b[0m\n\u001b[1;32m   1237\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1240\u001b[0m     \u001b[0mtest_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/svm/base.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0matleast2d_or_csr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36matleast2d_or_csr\u001b[0;34m(X, dtype, order, copy, force_all_finite)\u001b[0m\n\u001b[1;32m    163\u001b[0m     return _atleast2d_or_sparse(X, dtype, order, copy, sp.csr_matrix,\n\u001b[1;32m    164\u001b[0m                                 \u001b[0;34m\"tocsr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misspmatrix_csr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                                 force_all_finite)\n\u001b[0m\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36m_atleast2d_or_sparse\u001b[0;34m(X, dtype, order, copy, sparse_class, convmethod, check_same_type, force_all_finite)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         X = array2d(X, dtype=dtype, order=order, copy=copy,\n\u001b[0;32m--> 142\u001b[0;31m                     force_all_finite=force_all_finite)\n\u001b[0m\u001b[1;32m    143\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36marray2d\u001b[0;34m(X, dtype, order, copy, force_all_finite)\u001b[0m\n\u001b[1;32m    118\u001b[0m         raise TypeError('A sparse matrix was passed, but dense data '\n\u001b[1;32m    119\u001b[0m                         'is required. Use X.toarray() to convert to dense.')\n\u001b[0;32m--> 120\u001b[0;31m     \u001b[0mX_2d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \"\"\"\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for float(): 2014-01-25 23:59:00"
     ]
    }
   ],
   "source": [
    "%time\n",
    "\n",
    "parameters = {'C':[.001, .01, .1, 1, 10, 100], 'gamma':[.0001, .001, .01, .1, 1, 10, 100]}\n",
    "\n",
    "SVR_model = svm.SVR(kernel='rbf')\n",
    "\n",
    "SVR_model_OptParams = grid_search.GridSearchCV(SVR_model, parameters)\n",
    "SVR_model_OptParams.fit(X_gridsearch,y_gridsearch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimum hyperparameters C and gamma:  {'C': 100, 'gamma': 0.01}\n",
      "N samples for grid search of hyperparameters C and gamma:  190\n"
     ]
    }
   ],
   "source": [
    "print 'Optimum hyperparameters C and gamma: ',SVR_model_OptParams.best_params_\n",
    "print 'N samples for grid search of hyperparameters C and gamma: ',len(X_gridsearch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5 µs, sys: 2 µs, total: 7 µs\n",
      "Wall time: 10 µs\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'SVR_model_OptParams' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-d33d1da2d3a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSVR_model_OptParams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'SVR_model_OptParams' is not defined"
     ]
    }
   ],
   "source": [
    "%time\n",
    "scores = cross_validation.cross_val_score(SVR_model_OptParams, X_train, y_train, cv=10)\n",
    "print(scores.min(), scores.mean(), scores.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate next-hour forecast (predict!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Use SVR model to calculate predicted next-hour usage\n",
    "predict_y_array = SVR_model_OptParams.best_estimator_.predict(X_test_scaled)\n",
    "\n",
    "# Put it in a Pandas dataframe for ease of use\n",
    "predict_y = pd.DataFrame(predict_y_array,columns=['USAGE'])\n",
    "predict_y.index = X_test_df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot time series of actual and predicted electricity demand over the testing period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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F6u/RTPMubwBv8uPjlkyzEEIIAVkMmq+55hoqKipYuHBh2utfffVVCgoKWLx4MYsXL+bH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oO+5iVrxN/texuHfYxmtzec29/WKHoaTpLbX2TSjU2lWUFBQUFhmpFdCGarsGbIyDfp6PH66fvY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\nvUZGQ04AAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the predicted values and actual\n",
    "import matplotlib.dates as dates\n",
    "\n",
    "plot_start = test_start\n",
    "plot_end = test_end\n",
    "\n",
    "fig = plt.figure(figsize=[12,5])\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(y_test_df.index,y_test_df,color=blue_light,linewidth=1)\n",
    "plt.plot(predict_y.index,predict_y,color=red_orange,linewidth=1)\n",
    "plt.ylabel('Electricity Usage (kWh)')\n",
    "plt.ylim([0,2])\n",
    "plt.legend(['Actual','Predicted'],loc='best')\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b %d'))\n",
    "\n",
    "#fig.savefig('output_plots/SVM_predict_TS.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot actual vs. prediced usage.  If prediction were perfect, all points would be on 45 degree line (y=x).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jCWonhKH4ErQLgV79UbQd0K3fgf5SGQnjB6Bt114MewKCRLHskK5CZIoKoKRI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\nw/n/WFIJ/qZlTEYAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4))\n",
    "plot = plt.plot(y_test_df,predict_y,color=red_orange,marker='.',linewidth=0,markersize=10,alpha=.4)\n",
    "plot45 = plt.plot([0,2],[0,2],'k')\n",
    "plt.xlim([0,2])\n",
    "plt.ylim([0,2])\n",
    "plt.xlabel('Actual Hourly Elec. Usage (kWh)')\n",
    "plt.ylabel('Predicted Hourly Elec. Usage (kWh)')\n",
    "\n",
    "#fig.savefig('output_plots/SVM_plot_errors.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plots the time series of actual and predicted electricity demand for a given time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5oyM76R13CIADGXnW93VxgRad5Hr0sSqO1DYq41VPSTULtq4VqoyXom5R2SxV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\nALAJhRcAAIBNKLwAAABs8v8aAz5nw5Kt7AAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_start = test_start\n",
    "#plot_end = test_end\n",
    "plot_start = '25-mar-2014 00:00:00'\n",
    "plot_end = '26-mar-2014 23:00:00'\n",
    "xticks = pd.date_range(start=plot_start, end=plot_end, freq='6H')\n",
    "\n",
    "fig = plt.figure(figsize=[10,4])\n",
    "ax = fig.add_subplot(111)\n",
    "plot1 = plt.plot(predict_y[plot_start:plot_end].index,predict_y[plot_start:plot_end],color=red_orange,linewidth=2)\n",
    "plot2 = plt.plot(y_test_df[plot_start:plot_end].index,y_test_df[plot_start:plot_end],color=blue_light,linewidth=2)\n",
    "#plt_predict = plt.plot(predict_y[plot_start:plot_end].index,predict_y[plot_start:plot_end])\n",
    "#plt_actual = plt.plot(y_test_df[plot_start:plot_end].index,y_test_df[plot_start:plot_end])\n",
    "plt.ylabel('Electricity Usage (kWh)')\n",
    "plt.xticks(xticks)\n",
    "plt.title('Support Vector Regression')\n",
    "plt.legend(['Predicted','Actual'],loc='best')\n",
    "#ax.xaxis.set_major_locator()\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%H:00 \\n%a \\n%b %d \\n \\n'))\n",
    "#dates.HourLocator(interval=12)\n",
    "\n",
    "\n",
    "#fig.savefig('output_plots/SVM_predict_TS_zoom.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bmpo2OPtlWFgYjh8/jsLCQtjZ2eHDDz9EZGQk3njjDTg7O8PDwwOffvqp+p+A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\nggRGjcVFAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot daily total kWh over testing period\n",
    "y_test_barplot_df = pd.DataFrame(y_test_df,columns=['USAGE'])\n",
    "y_test_barplot_df['Predicted'] = predict_y['USAGE']\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "y_test_barplot_df.resample('d',how='sum').plot(kind='bar',ax=ax,color=[blue_light,red_orange])\n",
    "ax.grid(False)\n",
    "ax.set_ylabel('Total Daily Electricity Usage (kWh)')\n",
    "ax.set_xlabel('')\n",
    "# Pandas/Matplotlib bar graphs convert xaxis to floats, so need a hack to get datetimes back\n",
    "ax.set_xticklabels([dt.strftime('%b %d') for dt in y_test_df.resample('d',how='sum').index.to_pydatetime()],rotation=0)\n",
    "\n",
    "#fig.savefig('output_plots/SVM_predict_DailyTotal.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are some accuracy measures.  These are moved to the file errors.py for ease of use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
